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Updated: Nov 1, 2025

Identification of Circular RNAs using RNA Sequencing

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

Bioinformatic Analysis of Circular RNA Expression.

Enrico Gaffo¹, Alessia Buratin^1,2, Anna Dal Molin¹

¹Department of Molecular Medicine, University of Padova, Padova, Italy.

Methods in Molecular Biology (Clifton, N.J.)

|June 23, 2021

Summary

Circular RNAs (circRNAs) are key regulators in cancer. This study presents a computational protocol for analyzing circRNA expression from RNA-seq data, aiding cancer research and biomarker development.

Keywords:

Bioinformatics Circular RNA Computational pipeline RNA-seq

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Last Updated: Nov 1, 2025

Identification of Circular RNAs using RNA Sequencing

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

Published on: October 21, 2022

Use of Alu Element Containing Minigenes to Analyze Circular RNAs

Use of Alu Element Containing Minigenes to Analyze Circular RNAs

Published on: March 10, 2020

Area of Science:

Molecular Biology
Bioinformatics
Cancer Research

Background:

Circular RNAs (circRNAs) are generated by backsplicing and regulate gene expression.
Dysregulated circRNA expression is implicated in cancer development and progression.
circRNAs hold potential as cancer biomarkers and therapeutic targets.

Purpose of the Study:

To present a state-of-the-art computational protocol for genome-wide circRNA analysis.
To enable accurate detection, quantification, and differential expression testing of circRNAs.
To guide the determination of circular transcript sequences and in silico functional characterization.

Main Methods:

Utilizing RNA-sequencing (RNA-seq) data.
Identifying circRNAs by detecting reads spanning backsplice junctions.
Employing computational protocols for genome-wide analysis.

Main Results:

A comprehensive protocol for circRNA analysis from RNA-seq data is provided.
The protocol facilitates circRNA detection, quantification, and differential expression analysis.
Methods for determining circular transcript sequences and functional characterization are outlined.

Conclusions:

The presented computational protocol is valuable for advancing circRNA research in cancer.
This approach supports the identification of novel cancer regulatory networks.
The protocol aids in the development of circRNA-based biomarkers for cancer diagnosis and monitoring.